Article Sidebar

Download
PDF
Statistic
Read Counter : 58 Download : 27

Main Article Content

Abstract

Urban mobility management is an issue that smart cities cannot ignore, and it requires reliable, sustained, and precise dynamic monitoring of traffic flows. This paper introduces a cost-effective mobile LiDAR-based methodology for three-dimensional urban traffic analysis, providing the high-resolution spatial data necessary for future AI-driven mobility decoding. We use real-world data acquisition rather than conventional studies that rely on traffic simulation tools, such as VISSIM or AIMSUN, to model traffic dynamics, including vehicle volumes, vehicle shapes, inter-vehicle distances, and automatic vehicle counting. The LiDAR system was a mobile system that used a terrestrial laser scanner (TLS) to capture high-density 3D point clouds at various urban intersections with no heavy infrastructure. The suggested methodology encompasses the whole processing chain, i.e., data collection, preprocessing, object segmentation, vehicle localization, volume estimation, and infrastructure element localization. The experiment at two intersections in the city of Tangier (Morocco) demonstrates that the obtained real-world LiDAR data is comprehensive, visually accurate, and suitable for training artificial intelligence models for traffic analysis and management. The proposed workflow provides a foundation of geometric data that could be used for future AI-based traffic analysis, following further annotation and model development.

Keywords

Urban traffic management Mobile lidar 3D point clouds Artificial intelligence Vehicle detection Smart cities

Article Details

How to Cite
Naoufal, S. ., Omar, A. ., & Brahim, B. . (2026). An affordable mobile LiDAR approach for efficient three-dimensional analysis of urban traffic. Future Technology, 5(2), 336–346. Retrieved from https://fupubco.com/futech/article/view/798
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. S. Busch, C. Koetsier, J. Axmann, and C. Brenner, “LUMPI: The Leibniz University Multi-Perspective Intersection Dataset,” in Proc. IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany, Jun. 2022, pp. 1127-1134. doi: 10.1109/IV51971.2022.9827157
  2. M. Tang, D. Yu, P. Li, C. Song, P. Zhao, W. Xiao, and N. Chen, “A Multi-Scene Roadside LiDAR Benchmark towards Digital Twins of Road Intersections,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 10, pp. 341-348, 2024. https://doi.org/10.5194/isprs-annals-X-4-2024-341-2024
  3. A. Jamshidnejad and M. J. Mahjoob, "Traffic simulation of an urban network system using agent-based modelling," 2011 IEEE Colloquium on Humanities, Science and Engineering, Penang, Malaysia, 2011, pp. 300-304, doi: 10.1109/CHUSER.2011.6163738.
  4. C. Wang, C. Wen, Y. Dai, S. Yu, and M. Liu, “Urban 3D modeling using mobile laser scanning: A review,” Virtual Reality & Intelligent Hardware, vol. 2, no. 3, pp. 175–212, 2020..
  5. R. Hoogendoorn and S. Hoogendoorn, “Traffic flow theory and modelling,” in Traffic Flow Theory, Elsevier, 2013, pp. 125-159.
  6. Vosselman, George, and Hans-Gerd Maas. Airborne and terrestrial laser scanning. Whittles publishing, 2010.
  7. https://doi.org/10.1080/17538947.2011.553487
  8. S. Gargoum and K. El-Basyouny, "Automated extraction of road features using LiDAR data: A review of LiDAR applications in transportation," 2017 4th International Conference on Transportation Information and Safety (ICTIS), Banff, AB, Canada, 2017, pp. 563-574, doi: 10.1109/ICTIS.2017.8047822.
  9. A. Nguyen and B. Le, "3D point cloud segmentation: A survey," 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), Manila, Philippines, 2013, pp. 225-230, doi: 10.1109/RAM.2013.6758588.
  10. GreenValley International, GreenValley International - Official Website. [Online]. Available: https://www.greenvalleyintl.com/
  11. J. Zhang and S. Singh, “LOAM: LiDAR Odometry and Mapping in Real-Time,” in Proc. Robotics: Science and Systems (RSS), Berkeley, CA, USA, 2014.
  12. T. Qin, P. Li, and S. Shen, “VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 1004-1020, Aug. 2018. doi: 10.1109/TRO.2018.2853729.
  13. M. Rutzinger, B. Höfle, and N. Pfeifer, “Object-based point cloud analysis of full-waveform airborne laser scanning data,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 6, pp. 760-769, 2011. https://doi.org/10.3390/s8084505
  14. CloudCompare Development Team, “CloudCompare—3D point cloud processing software,” Version 2.13, 2024. [Online]. Available: https://www.cloudcompare.org
  15. Autodesk Inc., “Autodesk ReCap Pro: Reality capture and point cloud processing,” 2023.
  16. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” in Proc. KDD, 1996, pp. 226-231.
  17. Y. Lin, J. Hyyppä, H. Kaartinen, and A. Kukko, “Performance analysis of mobile laser scanning systems in target representation,” Remote Sensing, vol. 5, no. 7, pp. 3140–3155, 2013.
  18. O. Kilani, M. Gouda, J. Weiß, and K. El-Basyouny, “Safety assessment of urban intersection sight distance using mobile LiDAR data,” Sustainability, vol. 13, no. 16, Art. no. 9259, 2021. https://doi.org/10.3390/su13169259
  19. R. Rajamani, Vehicle Dynamics and Control, 2nd ed. New York, NY, USA: Springer, 2012. https://doi.org/10.1007/978-1-4614-1433-9
  20. A. Kukko, H. Kaartinen, J. Hyyppä, and Y. Chen, “Multiplatform mobile laser scanning: Usability and performance,” Sensors, vol. 12, no. 9, pp. 11712-11733, 2012. https://doi.org/10.3390/s120911712
  21. J. Liu, X. Mao, Y. Fang, D. Zhu and M. Q. . -H. Meng, "A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving," 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China, 2021, pp. 978-985, doi: 10.1109/ROBIO54168.2021.9739407.
  22. F. Tao, Q. Qi, A. Liu, and A. Kusiak, “Digital twins and cyber-physical systems toward smart cities,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405-2416, 2019. https://doi.org/10.1016/j.eng.2019.01.014
  23. H. Ma, Z. Pei, Z. Wei, and R. Zhong, “Automatic extraction of road markings from mobile laser scanning data,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. 42, pp. 825-830, 2017. https://doi.org/10.5194/isprs-archives-XLII-2-W7-825-2017
  24. Cavoukian, A. (2009). Privacy by design: The 7 foundational principles. Information and privacy commissioner of Ontario, Canada, 5(2009), 12.
  25. T. T. Vu, F. Yamazaki, and M. Matsuoka, “Multi-scale solution for building extraction from LiDAR and image data,” Int. J. Appl. Earth Observ. Geoinf., vol. 11, no. 4, pp. 281-289, 2009. https://doi.org/10.1016/j.jag.2009.03.005
  26. E. Binshaflout, C. Zaghouani, N. Guefrachi, C. Antoniadis, H. Ghazzai, A. Alsharoa, and G. Setti, “Annotated 3D Point Cloud Dataset for Traffic Management in Simulated Urban Intersections,” in Proc. IEEE ISCAS, London, UK, May 2025, pp. 1-5. doi: 10.1109/ISCAS56072.2025.11044184.